Networks and interconnectivity of individuals, groups, and organizations have dramatically increased in recent years. The Internet connects the world by joining users that represent various entities, information, and resources. These connected users form enormous banks of resources, resulting in a world wide web of users. The users store and access data files, documents, and Web pages containing various content.
The growth of the Internet has created many opportunities for users to uncover content and other resources related to their interests. Likewise, the growth has created opportunities for Web service providers to seek out users that may be interested in obtaining resources from the Web service provider. Users and providers communicate electronically, often exchanging resources and conducting electronic commerce. Web technology has made it possible to target information and resources to users with specific interests.
Targeting users with specific interests seeks to make the exchange of information and electronic commerce more efficient. Users receive materials related to their interests, while topics and materials in which they are not interested are sent to others. Targeting users seeks to reduce the burden on users who may ultimately consume products and services of the Web service providers. Targeting users helps alleviate the volumes and volumes of potential providers. To reduce the number of irrelevant product providers and to increase the quality of a consumer's interaction with relevant Web service providers, information regarding potential consumers may be filtered to deliver the most relevant materials to the user. Additionally, by properly targeting likely users, Web service providers may more efficiently focus their marketing and sales efforts.
Information filtering may be performed in a number of ways. For example, a customary consumer telephone directory of businesses, such as the Yellow Pages, filters product providers by geographic calling area. Further, Web Service Providers and Internet portals also classify information by categorizing Web pages by topics such as news, sports, entertainment, and the like. However, these broad subject areas are not always sufficient to locate information of interest to a consumer.
More sophisticated techniques for filtering products and services of interest to consumers may be employed by identifying information about the user. These methods may monitor and record a consumer's purchase behavior or other patterns of behavior. Information may be collected by means of surveys, questionnaires, opinion polls, and the like. These conventional techniques may be extrapolated to the networked world by means of inferential tracking programs, cookies, and other techniques designed to obtain consumer information with minimal consumer effort and minimal expenditure of provider resources.
Filtering methods serve to organize the array of information, goods, and services to assist the user by presenting materials that the user is more likely to be interested in, or by directing the user to materials that the user may find useful. Filtering attempts to sift through the vast stores of information while detecting and uncovering less conspicuous information that may be of interest to the user. Filtering methods attempt to locate items of meaningful information that would otherwise be obscured by the volume of irrelevant information vying for the attention of the user.
Information filtering may be directed to content-based filtering where keywords or key articles are examined and semantic and syntactic information are used to determine a user's interests. Additionally, expert systems may be utilized to “learn” a user's behavior patterns. For example, expert systems or intelligent software agents may note a user's actions in response to a variety of stimuli and then respond in the same manner when similar stimuli present in the future.
As expert systems grow, or as intelligent software agents expand to cover additional users or groups, the range and accuracy of the responses may be refined to increase the efficiency of the system. Collaboration among users or groups of like users results in increased accuracy with regard to predicting future user responses based upon past responses. Evaluating feedback of other similar users is effective in determining how a similar user will respond to similar stimuli. Users that agreed in the past will likely agree in the future. These collaborative filtering methods may use ratings for articles such as information, goods, services, and the like, to predict whether an article is relevant to a particular user.
Information may be transferred and stored on a consumer's computer by a Web server to monitor and record information related to a user's Web-related activities. The user's Web-related information may include information about product browsing, product selections, purchases made by the user at Web pages hosted by a Web server. The information stored by the inferential tracking programs is typically accessed and used by the Web server when the particular server or Web page is again accessed by the user computer. Cookies may be used by Web servers to identify users, to instruct the server to send a customized version of the requested Web page to the client computer, to submit account information for the user, and so forth. Explicit and implicit user information collection techniques may be used by Web-based providers of goods and services. In some instances, user information gathered by the servers is used to create personalized profiles for the users. The customized profiles are then used to summarize the user's activities at one or more Web pages associated with the server.
Current content advisory systems often focus on enhanced shopping carts to provide suggested additional products a user may purchase, while others have developed advisory systems to provide product recommendations based in part on a vendor payment to the Web-based provider to sort and move the vendor's product to the top of the list of recommended products or services.
Conventional content advisory systems focus on a point of sale event and only take into account a user's imminent product purchase and possibly prior purchases from the specific merchant. These prior systems do not cover all related digital content a user or users with similar activity patterns, may have acquired from a variety of sources.
Filtering methods based upon the content of the user's activities may be used to reach information, goods, and services for the user based upon correlations between the user's activities and the items. The filtering methods and customized profiles may then be used to recommend or suggest additional information, goods, and services in which the user may be interested.
These conventional systems may not utilize user profile information based on collected demographics, user ratings, editorial classifications, and behavioral data. Because they lack this additional data, typical advisory systems do not factor it into their recommendations.
The ability to accurately profile and target a user or a collection of similar users of a Web site is a difficult problem. Registration data, including demographic information, forms a component of this analysis, however, most users do not register or complete the registration form, and the data collected is not updated based on a user's current interests. Behavioral data gathered from a user's activity on a Web site can provide a more current indication of a user's interest, however, it is difficult to classify the documents or actions taken by a user unless the information is tagged or categorized based on its content and contextual meta-data.